AIMC Topic: Pathology, Clinical

Clear Filters Showing 51 to 60 of 87 articles

Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology.

Nature reviews. Clinical oncology
In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks m...

Machine learning for tissue diagnostics in oncology: brave new world.

British journal of cancer
Machine learning is an exciting technology with broad application in big data analysis, as well as increasingly in specialised healthcare. As a diagnostic tool in tissue workup and pathology, it has the potential for personalised and stratified appro...

Machine learning approaches for pathologic diagnosis.

Virchows Archiv : an international journal of pathology
Machine learning techniques, especially deep learning techniques such as convolutional neural networks, have been successfully applied to general image recognitions since their overwhelming performance at the 2012 ImageNet Large Scale Visual Recognit...

Pathology Image Analysis Using Segmentation Deep Learning Algorithms.

The American journal of pathology
With the rapid development of image scanning techniques and visualization software, whole slide imaging (WSI) is becoming a routine diagnostic method. Accelerating clinical diagnosis from pathology images and automating image analysis efficiently and...

Deep-Learning Language-Modeling Approach for Automated, Personalized, and Iterative Radiology-Pathology Correlation.

Journal of the American College of Radiology : JACR
PURPOSE: Radiology-pathology correlation has long been foundational to continuing education, peer learning, quality assurance, and multidisciplinary patient care. The objective of this study was to determine whether modern deep-learning language-mode...

Impact of pre-analytical variables on deep learning accuracy in histopathology.

Histopathology
AIMS: Machine learning (ML) binary classification in diagnostic histopathology is an area of intense investigation. Several assumptions, including training image quality/format and the number of training images required, appear to be similar in many ...

Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer.

Cellular oncology (Dordrecht, Netherlands)
PURPOSE: Tumor-stroma ratio (TSR) serves as an independent prognostic factor in colorectal cancer and other solid malignancies. The recent introduction of digital pathology in routine tissue diagnostics holds opportunities for automated TSR analysis....

[The age of artificial intelligence in lung cancer pathology: Between hope, gloom and perspectives].

Annales de pathologie
Histopathology is the fundamental tool of pathology used for more than a century to establish the final diagnosis of lung cancer. In addition, the phenotypic data contained in the histological images reflects the overall effect of molecular alteratio...

Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists.

Archives of pathology & laboratory medicine
CONTEXT.—: Nodal metastasis of a primary tumor influences therapy decisions for a variety of cancers. Histologic identification of tumor cells in lymph nodes can be laborious and error-prone, especially for small tumor foci.